%0 Book Section %1 russ2008ifip %A Ruß, Georg %A Kruse, Rudolf %A Schneider, Martin %A Wagner, Peter %B Artificial Intelligence in Theory and Practice II %D 2008 %E Bramer, Max %I Springer %K Data_Mining Neural_Networks Precision_Agriculture Prediction %P 109--118 %R 10.1007/978-0-387-09695-7_11 %T Estimation of Neural Network Parameters for Wheat Yield Prediction %U http://dx.doi.org/10.1007/978-0-387-09695-7\_11 %V 276 %X Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays’ technology to agriculture. Due to the use of sensors and GPS technology, in today’s agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. In particular, the use of feed-forward backpropagation neural networks will be evaluated and suitable parameters will be estimated. In consequence, yield prediction is enabled based on cheaply available site data. Based on this prediction, economic or environmental optimization of, e.g., fertilization can be carried out.

@incollection{russ2008ifip, abstract = {Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays’ technology to agriculture. Due to the use of sensors and GPS technology, in today’s agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. In particular, the use of feed-forward backpropagation neural networks will be evaluated and suitable parameters will be estimated. In consequence, yield prediction is enabled based on cheaply available site data. Based on this prediction, economic or environmental optimization of, e.g., fertilization can be carried out.}, added-at = {2008-08-14T10:10:37.000+0200}, author = {Ru{\ss}, Georg and Kruse, Rudolf and Schneider, Martin and Wagner, Peter}, biburl = {https://www.bibsonomy.org/bibtex/2e79b7dfc1fef23f7fe1483cf428de004/gr650}, booktitle = {Artificial Intelligence in Theory and Practice II}, doi = {10.1007/978-0-387-09695-7_11}, editor = {Bramer, Max}, interhash = {a16b7690f0464418275ce7763b9672f9}, intrahash = {e79b7dfc1fef23f7fe1483cf428de004}, keywords = {Data_Mining Neural_Networks Precision_Agriculture Prediction}, month = {July}, pages = {109--118}, publisher = {Springer}, series = {IFIP International Federation for Information Processing}, timestamp = {2008-08-14T10:10:37.000+0200}, title = {Estimation of Neural Network Parameters for Wheat Yield Prediction}, url = {http://dx.doi.org/10.1007/978-0-387-09695-7\_11}, volume = 276, year = 2008 }